In the last fifty years, a tremendous amount of research has begun to elucidate the causes, characteristics and treatments of many neuropsychiatric disorders. Unfortunately, this process has not efficiently translated into effective patient treatments. The term “treatment resistance” is more the norm than the exception in psychiatry. For instance, in the Catie trials, which looked at the effectiveness of medications in schizophrenia, more than 75% of patients discontinued medication within 18 months, and in the Star-D trials more than 50% of patients with depression did not achieve remission despite the use of two or more antidepressants or use of an augmenting agent. This “treatment resistance” may be due in part to the fact that neuropsychiatric disorders are notoriously difficult to diagnose because existing categories of disorders are imprecise; there is a great deal of overlap and comorbidity in these conditions. It is also relevant to note that in randomized clinical trials, a significant majority of patients (85%) would need to be excluded because of a comorbid psychiatric or medical condition. Thus, it is difficult to accurately categorize patients. In general, categorical tests for neuropsychiatric disorders have not proven effective in accurately diagnosing and treating patients, as there is a great deal of variation in patient outcomes between patients categorized with the same diagnosis. Most psychiatric classification systems, which are based upon categorical assessments, do not take into account that most mental disorders are dimensional with similar and overlapping symptoms in patients with discrete diagnostic categories. Even in research settings, the limitations of current categorical nosology of psychiatric disorders results in a disconnect between trial findings and application to real world settings. The problem of treatment resistance in psychiatry, the lack of biomarkers for diagnosis, the fact of similar symptoms in different categorical diagnoses, and the difficulty in drawing boundaries between disorders, all necessitates a paradigm shift from categorical to dimensional diagnostics. This dimensional approach, which is herein described, focuses on dimensional symptoms which are correlated with their biological underpinnings. This model incorporates subsets of psychiatric symptoms across cognitive, affective and subcortical regional locations, and which can thus be identified by objective biomarkers. These biomarkers, designed and described to identify neurobiological alterations in the domains of brain structure, physiology and neurochemistry, reflect diverse pathophysiological pathways from genome to phenome.
Virtually all brain disorders may cause psychiatric symptoms. The term “neuropsychiatric disorders” may refer to brain disease or dysfunction that causes psychiatric symptoms. Examples of neuropsychiatric disorders include depression (including treatment resistant depression, bipolar depression, etc. . . . ), schizophrenia, PTSD and other anxiety disorders, autism, ADHD, and the like.
Although various research and clinical studies have looked for diagnostic and therapeutic indicators in an almost overwhelming variety of genomic markers, gene expression markers and protein markers, this vast and growing body of data has proven difficult to interpret. Most physicians are unable to synthesize the tremendous amount of information on possible risk factors and indicators in order to apply this information clinically to diagnose and/or treat patients. Thus, there is an as yet unmet need for reports, panels and/or kits that would allow a medical professional to apply the most relevant genetic, epigenetic, transcriptomic, proteomic and functional imaging tests in a meaningful manner to their patients. It is also critical to provide tests that allow the medical profession to understand and interpret the results of such tests, as well as have a resource to call upon for clarification of their interpretations.
Described herein are systems and kits, including panels, assays and articles of manufacture, including reports and the use of expert-driven information help centers, which meet this need by providing interpretive and directed reports, particularly for the treatment of neuropsychiatric disorders such as depression. Because of the confusing and contradictory information available for even those genes established as implicated for treatment of depression and dementia, it would be beneficial to provide systems that (1) select the relevant genes, gene families, and/or pathways, epigenetic markers, and/or protein/expression markers; (2) provides information or links to the key information such as the relevance and meaning of each indicator or screen member; (3) suggests or provide relevant therapies based on the results of these tests; (4) provide an indication of the confidence/reliability of the interpretive information provided; (5) provide additional interactive information (e.g., a help center) that can answer questions as they relate specific test results to accurately apply the information to their patients. In particular, described herein are methods and articles of manufacture that may provide a concise reporting to a medical professional to help make diagnostic and/or therapeutic decisions.
The interpretive reports described herein may also be useful in developing and understanding new sites of action, association, and/or patient response to psychotropic drugs, as well as a previously undisclosed explanations on how genomic variation, such as single nucleotide polymorphisms (SNPs), small tandem repeats (STRs), variable tandem repeats (VNTRs), copy number variants (CNVs), insertion/deletions (indels), rare variants, chromosomal duplications/deletions, CpG islands and shores, allele specific methylation, and the like, throughout the genome, as well as in specific genes, gene families, and/or pathways visualized by brain imaging procedures, are related to subtypes of psychiatric disorders, and the relative response to different classes of therapeutic agents. There is a growing need to provide an interpretation of information provided by genetic testing (particularly multiple genetic tests) to the clinician, or learned intermediary, to aid in treatment and/or diagnosis. The articles of manufacture described herein may include interpretive logic configured to analyze the results of all of the assays and to provide interpretive comments, wherein the interpretive logic is encoded for processing on a processor or any other easily accessed and reviewable form. The interpretive comments may indicate the effect of any identified genomic variant on the regulation of neurotransmitter activity, ionic channel function and/or metabolism. Providing this information may allow a physician to properly understand the interpretation of a genomic variation, and may allow compliance with regulatory guidelines. Unfortunately, without providing a proper context, genomic test results can lead to confusion rather than clarification, particularly in a clinical setting. In subsequent paragraphs, particular language of interpretation for various genomic biomarker test results will be provided. Within these descriptions, clarification regarding both the potential benefits and limitations of biomarker analysis is provided, as well as recommended therapeutic interventions based upon the genome or other relevant biomarker of the patient.
Genes associated with neurotransmitters, ionic channels (calcium, sodium and potassium) and metabolic pathways (immune and inflammatory), have been found to be abnormal in patients with various neuropsychiatric disorders. For instance, genes which regulate serotonin pathways, including genes coding for receptors, metabolism and reuptake mechanisms, are associated with mood disturbances. Furthermore, other genetic-neurotransmitter pathways, including dopamine, norepinephrine and glutamate may be associated with depression or risk of dementia. Regarding ion channels, pathological states in the brain can result from changes in which alter membrane excitability. Phenomenologically, alterations in ion channels may be seen clinically as paroxysmal, recurrent, or intermittent disturbances. Genes related to cerebral metabolism, such as methylation and the like, also impart changes with neuropsychiatric implications. For example, genes related to oxidation, mitochondrial function, proteasomal degradation and insulin and its associated second messenger systems (gene pathways) may also have nuropsychiatirc implaications. Unfortunately, what is not well-understood is how to apply such genetic or expression-related information to patient treatment in a robust and useful manner, particularly for neuropsychiatric disorders. Genes which regulate immune processes are also relevant in clinical assessments as variants in glial cell activity have been associated with depression, schizophrenia, bipolar disease and dementia.
Unfortunately, the heterogeneous nature of gene findings in neuropsychiatric disorders suggests that neuropsychiatric disorders themselves, as mentioned above, are heterogeneous and require a dimensional, rather than categorical approach. By analyzing disorders using a spectrum of biomarkers, such as SNP-based gene analysis, subtypes of neuropsychiatric conditions can be differentiated and treated in a personalized manner. This analysis may allow a deeper understanding of a patient's health across a variety of neuropsychiatric categories. Further, the employment of such analysis will allow mental health professionals to treat individuals with more specific and targeted interventions. Therefore, the approach described herein may be used to reveal genomic influences on trait components of a variety of neuropsychiatric disorders (regardless of categorical classification) and may help identify subpopulations of patients that can benefit from more targeted pharmacotherapy. This approach has proven difficult, however, at least because it is difficult to know which collection of biomarkers are sufficient and useful for this purpose.
As an example, a genomic variation in one of the family of genes that regulates the dopamine pathway can be associated with reduced levels of this neurotransmitter, along with parallel changes in an individual's behavior. Patients with variation in the dopamine pathway differ not only in their symptoms, but also in their response to therapies as well. However, such patients may be otherwise hard to identify based solely on their behavior. By examining a variety of biomarkers, a deeper understanding of a patient's treatment needs may be achieved. For example, a mood complaint, such as depression, can be a consequence of genomic defects that affect the metabolism of the serotonin pathway, or can be a consequence of a genomic defect that regulates the dopamine pathway, the glutamate pathway, or some other pathway that affects neurotransmitter metabolism. As a similar example, depression can be etiologically associated with a genomic variation in the glutamate pathway in one individual, and with a genomic variation related to the dopamine or the norepinephrine pathway in another. Thus, it would be beneficial to provide a method and articles of manufacture (including systems, reports, kits and the like) that are capable of conveniently, effectively and efficiently informing a physician on a variety of relevant factors that will guide patient care.
The recognition of the distinction in the genomic heterogeneity related to the expression of subtypes of psychiatric disorders has important therapeutic implications. Frequently, an individual with a mood disturbance does not respond favorably to a specific first class of therapeutic agents, but may respond to a different second class of therapeutic agents. As an example, an individual who is experiencing depression due to a genomic variant in the dopamine pathway that causes a metabolic defect will not respond, or will respond less favorably, to a serotonin modulating agent. In clinical practice, this can happen when a psychiatrist treats a patient with depression who possesses genomic variation associated with a dopamine-related defect with a serotonin modulating drug, like sertraline or paroxetine, instead of a dopamine modulating drug such as buproprion. In these instances, the drug may produce a worsening of symptoms instead of improving them.
Conversely, an individual with genomic variation associated with the metabolism of the serotonin pathway will respond less favorably to a dopamine modulating agent. Frequently in such patients, depressive symptoms will not improve or may, in fact, worsen. Unfortunately, psychiatrists currently administer medications for depression solely on a trial and error basis. The lack of diagnostic specificity frequently leads to ineffective treatments or a delay in the proper treatment.
Thus, a common problem in the management of all psychiatric disorders is a lack of diagnostic specificity and/or treatments which are not coupled to the unique neurobiological mechanisms associated with psychopathology. Provided herein is a method of using the analysis of biomarkers as an aid to diagnosis and as a choice of therapeutic treatment.
It is further an object of this description to set forth a dimensional model based upon the specific functional axes related to neurochemical pathways and anatomical regions in the brain that are causally associated with various neuropsychiatric conditions. These axes each have associated genomic, epigenetic, transcriptomic, proteomic, metabolomic or brain imaging biomarkers which may be probed. The ability to accurately identify variations of functionally related biomarkers, as taught herein, represents an important advance in the field of mental health.
Lab diagnostics in central nervous system (CNS) disorders often lack specificity and sensitivity. A novel solution described herein is to recognize that an integrated approach to the diagnosis of these disorders, rather than a single lab modality, may be anticipated. Thus, while there may be limitations to diagnosing a disorder based upon the utilization of genomic-based technology exclusively, the application of an analysis of biomarker signals integrated under a broader diagnostic framework, which includes one or more of genomic analysis, epigenetic analysis, transcriptomic analysis, proteomic analysis, metabolomics analysis, and the like, will increase the confidence of the diagnostic signal and lead to previously unrealized treatment efficacy and specificity. These should also be recognized as part of the dimensional model herein described.
For example, it has long been suspected that a cluster of genes is likely to contribute to a gene dosing effect in schizophrenia. However, even when detecting these genes, it is unclear whether any of these genes are actually expressed. Thus, it is not sufficient to know that a patient has a genetic polymorphism linked to an increase in risk; there is a requirement in the field to develop a more holistic analysis which should also include, in addition to risk genes, the actual detection of altered gene expression. Gene expression, in addition to gene inheritance, may provide a more reliable use of biomarkers in neuropsychiatry to understand more fully the dimensional model herein described.
Expression may be based upon unique transcriptional analysis, including epigenetics, e.g., methylation of specific CpG islands and allele-specific methylation, and post translational modifications and/or protein expression and modification. Several examples will be set forth below related to specific disorders but the teaching can be more generally applied to other conditions not mentioned.
Thus, in some embodiments, the approach outlined is a teaching which requires an analysis at multiple levels of molecular biology: autosomal and sex-chromosomal variation, methylation and/or histone modifications, transcriptomics, proteomics, metabolomics and the like. Each of these signals can be incorporated to provide a more complete understanding to a specific patient
Treatment resistance in psychiatry is an area where there is a particular pressing need to provide biomarker-based tests that collects relevant biomarkers and presents the results of these biomarkers to a physician in an interpreted manner. As a specific example, within a 15-month period after having been diagnosed with depression, sufferers are four times more likely to die as those who do not have depression. Almost 60% of suicides have their roots in major depression, and 15% of those admitted to a psychiatric hospital for depression eventually kill themselves. In the U.S. alone, the estimated economic costs for depression in 1990 exceeded $44 billion. The World Health Organization estimates that major depression is the fourth most important cause worldwide of loss in disability-adjusted life years, and will be the second most important cause by 2020.
A variety of pharmacologic agents are available for the treatment of depression. Significant success has been achieved through the use of serotonin reuptake inhibitors (SRIs), norepinephrine reuptake inhibitors (NERIs), combined serotonin-norepinephrine reuptake inhibitors (SNRIs), monoamine oxidase inhibitors (MAOIs), glutamate inhibitors and/or other compounds. However, even with these options available, many patients fail to respond, or respond only partially to treatment. Additionally, many of these agents show delayed onset of activity, so that patients are required to undergo treatment for weeks or months before receiving benefits.
Traditional therapies can also have significant side effects. For example, more than a third of patients taking SRIs experience sexual dysfunction. Other problematic side effects include gastrointestinal disturbances, often manifested as nausea and occasional vomiting, agitation, insomnia, weight gain, and/or the onset of diabetes.
Patients that fail to respond to these standard/traditional depression therapies may be classified as suffering from treatment resistant depression (TRD, also referred to as refractory depression or treatment refractory depression). TRD is often described as depression that does not respond to different antidepressant medications from more than at least two different classes, or different treatments.
In the clinic, 40-50% of depressed patients who are initially prescribed antidepressant therapy do not experience a timely remission of depression symptoms. This group typifies treatment-refractory depression, that is, a failure to demonstrate an “adequate” response to an “adequate” treatment trial (that is, sufficient intensity of treatment for sufficient duration)). Moreover, about 20-30% of depressed patients remain partially or totally resistant to pharmacological treatment.
There is increasing evidence implicating the role of neurotransmitters in depression, in particular the monoamines serotonin, noradrenaline, dopamine, as well as the excitatory amino acid glutamate. Many of the tricylic antidepressants (TCAs), selective serotonin reuptake inhibitors (SSRIs) and monoamine oxidase inhibitors (MAOIs) effective in the treatment of depression increase the availability of the catecholamines (noradrenaline and dopamine) and indolamines (serotonin) in the CNS. The clinical efficacy of these agents has given rise to the catecholamine-indolamine hypothesis of depression. This theory postulates that a certain level of amines and/or receptor sensitivity to catecholamines functions to generate a normal mood. Receptor insensitivity, a depletion of monoamines, or a decrease in their release, synthesis or storage has been postulated to lead to depression.
Personalized medicine is considered a young but rapidly advancing field of healthcare that is informed by each person's unique clinical, genomic, and environmental information. Because these factors are different for every person, the nature of diseases—including their onset, their course, and how they might respond to drugs or other interventions—is as individual as the people who have them.
The goal of personalized medicine is to customize or individualize treatments based on the particular environmental, genomic profile, and clinical information specific to a patient, thereby allowing accurate predictions to be made about a person's susceptibility of developing disease, the course of disease, and its response to treatment.
In order for personalized medicine to be used effectively by healthcare providers and their patients, these findings must be translated into precise diagnostic tests and targeted therapies. This has begun to happen in certain areas, such as testing patients genetically to determine their likelihood of having a serious adverse reaction to various cancer drugs. Recently, work has begun to extend this testing to drugs used in other fields, including psychopharmacology.
The complete sequencing of the human genome provided a first step towards understanding the biological workings behind countless medical conditions. Although the field of personalized medicine is advancing at a fast pace, as new disorders are linked to particular genetic predispositions and mutations, adoption of such personalized markers for disorders and treatments has been slowed by the overwhelming amount of information available.
Although personalized medicine offers patients and clinicians numerous advantages, there are also increasing risks arising from the narrow focus and resulting myopia when examining complex disorders in light of only a few genomic associations. Medical practitioners are often caught between having too little information or too much information. If a medical practitioner examines only some of the genes which may be implicated in a disorder, he or she may miss essential information. Alternatively, providing information about too many contributing variations may prevent a concise diagnosis, resulting in confusion.
These problems are particularly present in the area of personalized medicine for use in treating psychiatric disorders. This patent application describes the heterogeneous nature of psychiatric conditions, and in particular some of the genomic variants and phenotypes that they are correlated with.
In particular, when prescribing treatment for resistance in psychiatry, there is a strong need to provide a means for reducing the overwhelming amount of genetic data available into a reduced and simplified format to guide a medical practitioner in treating these disorders.